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[General] AIP-210 Popular Exams & AIP-210 Related Certifications

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【General】 AIP-210 Popular Exams & AIP-210 Related Certifications

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 2
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 3
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 4
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 5
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 6
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q16-Q21):NEW QUESTION # 16
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: B
Explanation:
Explanation
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.

NEW QUESTION # 17
Which two of the following decrease technical debt in ML systems? (Select two.)
  • A. Boundary erosion
  • B. Refactoring
  • C. Design anti-patterns
  • D. Documentation readability
  • E. Model complexity
Answer: B,D
Explanation:
Explanation
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers.
Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.

NEW QUESTION # 18
In a self-driving car company, ML engineers want to develop a model for dynamic pathing. Which of following approaches would be optimal for this task?
  • A. Unsupervised Learning
  • B. Reinforcement learning
  • C. Dijkstra Algorithm
  • D. Supervised Learning.
Answer: B
Explanation:
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment.
Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .

NEW QUESTION # 19
You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
  • A. Logistic regression
  • B. K-nearest neighbors
  • C. Lasso regression
  • D. K-means
  • E. Ridge regression
Answer: C,E
Explanation:
Lasso regression and ridge regression are both types of linear regression models that can handle high- dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. References: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]

NEW QUESTION # 20
Which of the following approaches is best if a limited portion of your training data is labeled?
  • A. Semi-supervised learning
  • B. Reinforcement learning
  • C. Probabilistic clustering
  • D. Dimensionality reduction
Answer: A
Explanation:
Semi-supervised learning is an approach that is best if a limited portion of your training data is labeled. Semi- supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model.
Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.

NEW QUESTION # 21
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